Generation Analytic Platforms How to embed disruptors in your - - PowerPoint PPT Presentation

Disruptors and their applicability to Next Generation Analytic Platforms How to embed disruptors in your business strategy?

October 6th, 2015

Ashish Verma, Hybrid Services and Innovation Leader, Deloitte Consulting LLP

1. An Unprecedented Opportunity 2. The Data Management Life Cycle 3. How disruptors are impacting industries? 4. Organizing to Succeed

Agenda

The market is still emerging and presents an enormous

• pportunity

While not necessarily new…an unprecedented

• pportunity

Evolution not revolution Confluence of advances lead to enormous breakthrough potential

Technology disruptors continue to have the impact on the business of tomorrow; today

Market momentum is rapidly growing :

 200+TB of stored data in every sector  60 billion intelligent devices with a forecast of 26 billion

connected devices by 20201

 Industry players with their own themes

• Cisco: “Internet of Everything - $14.6 trillion value at

stake by 2022”

• GE: “Industrial Internet + analytics”
• IBM: “Smarter Planet”

 Rapidly forming ecosystem offerings and partnerships

due to early stage of maturity

• Cloudera, Intel Partnership, May 2014
• EMC Pivotal along with GE, Intel, Accenture, AT&T,
• Cisco. September 2013
• IBM and Technicolor IoT and M2M cloud solution, Jan

2014

• AT&T & Qualcomm to enable and connect consumer

IoT devices, Jan 2014

Real Time Decisioning Big Data Cloud Predictive Analytics In Memory Cyber Security and Privacy Machine Learning Cognitive Computing Wearables IoT

Sources: 1) Gartner, Nov. 2013

Disruptors

Within every organization understanding and applying disruptors to key Business Triggers is critical to staying relevant

Disruptor Key Business Triggers Key Technology Big Data

• Handling data volumes that are more than 10 TB
• Data with a changing structure, or no structure at all
• Very high throughput systems, with millions of concurrent users and

thousands of queries per second

• Business requirements that differ from the relational database model,

for example swapping ACID (Atomicity, Consistency, Isolation, Durability) for BASE (Basically Available, Soft State, Eventually Consistent)

• Processing of machine learning queries that are inefficient or

impossible to express using SQL Hadoop Cloudera HortonWorks IBM Big Insights Oracle Big Data Appliance NoSQL Data Stores i.e. MongoDB, Cassandra

Real Time Decisioning

• Increase service velocity for the business by embedding analytics into

the operational processes to support frontline decision making based

• n real-time events
• Provide a mechanism to route and correlate events in real time even

in scenarios of large volumes of data Apache Kafka Apache Storm Apache Spark SAP Real Time Offer Management Oracle Real Time Decisions

Predictive Analytics

• Predictive techniques enable strategic decision making by providing

future insights based on large volumes of structured and un- structured data. Examples include forecasting sales effectiveness by forecasting customer behavior, forecasting product demand, etc. SAS Predictive Analytics SalesForce (Analytics) Wave Cloud IBM SPSS RapidMiner Oracle Advanced Analytics Oracle Visual Analyzer SAP Visual Insights R

Within every organization understanding and applying disruptors to key Business Triggers is critical to staying relevant

Disruptor Key Business Triggers Key Technology Cloud

• Rapid implementation: Less time is required to get up and running
• n cloud-based systems
• Cost predictability: Cloud’s pay-as-you-go model makes it easier to

predict IT costs

• Balanced ROI: Cloud delivers a faster return on IT investments,

thanks to accelerated implementation and elimination of upfront licensing and infrastructure costs

• Agility: Companies can quickly develop and deploy new IT

capabilities and business processes to stay ahead of the competition and keep pace with changes in the marketplace

• Scalability: Cloud provides a flexible platform that can grow or shrink

as needed, enabling businesses to explore new markets, pursue new innovations and serve new customer segments Amazon Web Services Microsoft Azure Dimension Data Google Cloud IBM Big Insights on Cloud HP Cloud Analytics Bluelock Salesforce.com

Cyber Security & Privacy

• Threat Awareness: Automated network and malware forensic

analysis are needed, as well as intelligence collection from honeypots

• r other ‘baiting’ operations
• Security Intelligence & Event Management Solutions: Detailed

logging and SIEM are also table stakes when it comes to building advanced cyber-threat management capabilities. The stream of event data, when combined with internal and external intelligence, can allow correlation, analysis, and subsequent detection of threats that would

• therwise go unnoticed
• Unstructured and semi-structured inputs and intelligence: Invest

in data collection and analysis solutions — allowing automated crawling and information parsing.

• Use cyber analytics — linked to threat rosters and known business

risks and fraud issues — to identify potential areas of escalating risk Identity, Credential, and Access Management(ICAM) solutions Security Information & Event Management (SIEM) solutions

Within every organization understanding and applying disruptors to key Business Triggers is critical to staying relevant

Disruptor Key Business Triggers Key Technology In Memory

• Reduce total cost of ownership because the shift from physical to

logical reduces the hardware footprint, allowing more than 40 times the data to be stored in the same finite space

• Thousand-fold improvement in query response times to transaction

processing speed increases of 20,000 times

• Crunch massive amounts of data in real time to improve

relationships with their customers

• In-memory responses are also more predictable, able to handle large

volumes and a mix of structured, semi-structured, and unstructured raw data

• Operating costs can also be cut both by reducing maintenance

needs and by streamlining the performance of employees using the technology Oracle Exalytics In-Memory Machine SAP HANA Kognitio Apache Spark

• Industries wrestling with massive amounts of unstructured data or

struggling to meet growing demand for real-time visibility should consider taking a look. Cognitive analytics can be a powerful way to bridge the gap between the intent of big data and the reality of practical decision making

• As the demand for real-time support in business decision making

intensifies, cognitive analytics will likely move to the forefront in high- stakes sectors and functions

• It can improve prediction accuracy, provide augmentation and scale

to human cognition, and allow tasks to be performed more efficiently (and automatically) via context-based suggestions IBM Watson Cognitive Scale

Cognitive Analytics

Within every organization understanding and applying disruptors to key Business Triggers is critical to staying relevant

Disruptor Key Business Triggers Key Technology Machine Learning

• Applications of machine learning vary in complexity, from simplistic

spam filters in emails to more complex forms such as the virtual employee that can function as a service i.e. desk employee in retail and customer care operations.

• These applications are aided by technologies such as natural

language processing, voice recognition, handwriting recognition, image processing, correlation analytics and quantum computing

• A whole range of products and services built on underlying

technology such as IBM’s Watson that can act as ‘Smart Advisors’ Mahout SAS R

IoT

• Support sensor driven decision analytics
• Provide product life extension (enabling product upgrades and

enhancements delivered via software commands) and automated support that significantly reduces costs

• Provide process improvements through continuous precise

adjustments in manufacturing lines

• Optimize resource consumption across networks

Wireless technologies (WiFi, Bluetooth, RFID) Sensors Cloud Storage and Processing Platforms with Machine Learning and Advanced Modeling Capabilities

Wearables

• Wearables value comes from introducing technology into previously

prohibitive environments — where safety, logistics, or even etiquette have constrained traditional technology solutions

• Wearables generate data in real time and intelligently push it to a

devices according to the user’s current context — just-in-time digital

• logistics. Such use cases suggest that wearables may be most

valuable in an organization’s operations, rather than in customer- facing applications

• Wearables can be the first seamless way to enable workers with

digital information — especially where hands-free utility offers a clear

• advantage. Using wearables, workers in harsh environmental

conditions can access data without removing gloves or create records without having to commit data to memory and then moving to sheltered workstation Google Glass mHealth Fitness & Activity trackers Smartwatches

1. An Unprecedented Opportunity 2. The Data Management Life Cycle 3. How disruptors are impacting industries? 4. Organizing to Succeed

Agenda

The Data Management Life Cycle from provisioning and storage of data to delivery of insights

Common Data Acquisition

Single source to acquire and cleanse structured and unstructured data

Common Data Services

Services to manage master data, including quality, security, privacy, and lineage

Data Management

Data stores, repositories, and provisioning points to supply clean data for processing

Business Semantic Layer

Logical and physical representations of information in meaningful ways for end users

Business Intelligence

User access to primarily structured data for operational and management reporting, and discovery

Performance Management

Business performance, planning, forecasting, consolidation, and strategic scorecards

Analytics

Descriptive, diagnostic, predictive, and prescriptive analytical insights

Visualization

User access to information in alternate ways to ease understanding and action

Infrastructure

Secure infrastructure, platforms, and software as a service in the cloud or on premise

Workflow & Orchestration

Services to control the flow of information across the environment and processing lifecycle

Reference Data Structured Data Unstructured Data

Knowing where disruptors apply impacts your choice

INFRASTRUCTURE ANALYTICS VISUALIZATION WORKFLOW BUSINESS INTELLIGENCE PERFORMANCE MANAGEMENT DATA PROVISIONING & EXCHANGES DATA PLATFORMS DATA SERVICES COMMON DATA ACQUISITION

BIG DATA PREDICTIVE ANALYTICS MACHINE LEARNING COGNITIVE ANALYTICS NLP & TEXT ANALYTICS REAL TIME DECISIONING CROWDSOURCING CLOUD VISUALIZAZTION DIGITAL BIG DATA INTERNET OF THINGS DIGITAL CROWDSOURCING WEARABLES CROWDSOURCING CLOUD CLOUD CYBER SECURITY & PRIVACY BIG DATA CLOUD CORE RENEWAL IN-MEMORY CLOUD SOCIAL WEARABLES INTERNET OF THINGS CLOUD BIG DATA CORE RENEWAL IN-MEMORY COGNITIVE ANALYTICS REAL TIME DECISIONING PREDICTIVE ANALYTICS AMPLIFIED INTELLIGENCE NLP & TEXT ANALYTICS CYBER SECURITY & PRIVACY COGNITIVE ANALYTICS MACHINE LEARNING NLP & TEXT ANALYTICS BIG DATA BIG DATA BIG DATA

Our hypothesis is understanding the problem type tied to data constructs of variety, volume and velocity directs the technology choice and not the other way round

Structured Low Batch Near Real Time Real Time Traditional Data Warehouse/Analytical Applications MPP Massively Parallel Processing

Technology Variety Volume Velocity Legacy

Structured Semi-Structured Un-Structured High Low High Low High Batch Batch Batch Batch Near Real Time Near Real Time Real Time Near Real Time Distributed Clusters MPP Massively Parallel Processing In-Memory In-Memory Appliances MPP Massively Parallel Processing Specialized MPP Massively Parallel Processing Distributed Clusters Distributed Clusters Specialized System

Next Generation Technologies

Traditional DW

Technology Choices as a result of disruptors

Structured Data Unstructured Data

INFRASTRUCTURE ANALYTICS VISUALIZATION WORKFLOW BUSINESS INTELLIGENCE PERFORMANCE MFMT. DATA PROVISIONING & EXCHANGES DATA PLATFORMS DATA SERVICES COMMON DATA ACQUISITION

BIG DATA PREDICTIVE ANALYTICS MACHINE LEARNING COGNITIVE ANALYTICS NLP & TEXT ANALYTICS REAL TIME DECISIONING CROWDSOURCING CLOUD VISUALIZAZTION DIGITAL BIG DATA INTERNET OF THINGS DIGITAL CROWDSOURCING WEARABLES CROWDSOURCING CLOUD CLOUD CYBER SECURITY & PRIVACY BIG DATA CLOUD CORE RENEWAL IN-MEMORY CLOUD SOCIAL WEARABLES INTERNET OF THINGS CLOUD BIG DATA CORE RENEWAL IN-MEMORY COGNITIVE ANALYTICS REAL TIME DECISIONING PREDICTIVE ANALYTICS AMPLIFIED INTELLIGENCE NLP & TEXT ANALYTICS CYBER SECURITY & PRIVACY COGNITIVE ANALYTICS MACHINE LEARNING NLP & TEXT ANALYTICS BIG DATA BIG DATA BIG DATA

ETL + SQOOP + SPARK + Rabbit MQ Cloud Provider + ML + Text Mining Kerberos + Sentry + Knox HDFS + NoSQL + Relational Data Store + In Memory API + Cloud Provider + Kerberos + Sentry + Knox API + Digital Strategy Cognitive Tools + ML + Tableau or Qlik + Digital Tableau or Qlik + Digital Cloud + NoSQL + In Memory API’s + ML + Cogntive

1. An Unprecedented Opportunity 2. The Data Management Life Cycle 3. How disruptors are impacting industries? 4. Organizing to Succeed

Agenda

Unique Industry Solutions…Common Characteristics

Dozens of distinct industry use cases with proven value

FINANCIAL SERVICES ENERGY & RESOURCES AUTO / TRANSPORTATION HEALTHCARE MANUFACTURING MILITARY SMART CITIES RETAIL

• Dealership of the

future

• Remote diagnostics
• Fleet management
• Autonomous vehicle
• Smart Grid (multiple)
• Wellhead
• ptimization
• Autonomous Mining
• Perf-based Insurance
• Personalized risk

profiles

• Retail banking
• Remote monitoring
• Patient experience
• Equipment

monitoring

• Hospital supply chain
• Wireless factory
• Preventative

maintenance

• Supply chain
• Connected

battlefield

• Supply chain
• Tailored offers
• Inventory

management

• Checkout optimization
• Supply chain
• Smart lighting
• Smart parking
• Smart waste

Real-time Analytics Network connectivity elements Connected Devices Mobile Applications Event Orchestration Edge Gateways

Shared components

Sensors Streaming Data

Impact

• More responsive to

citizens’ needs

• Better control over
• perations
• Improved supplier

relationships

Responsive City Initiative Client: Municipality

Approach

• Implemented Technology functionality,

customized to fit the city’s unique needs

• Streamlined admin tasks and improved

coordination for 3,000 employees

• Mobile apps enable citizens to report issues

and inspectors to efficiently do their jobs

A citizen reports a damaged sidewalk using a smartphone. The system receives the information and finds more notifications related to the same area: there is garbage pending collection and an uncovered storm

• drain. An inspector goes to the reported address to

verify the received information and updates the information in the system. Based on the given input, the system determines the right provider to perform the corresponding maintenance tasks Once the maintenance tasks are finished, an inspector audits the work and submits their report into the system. The reported incidents have been solved. The work has been done efficiently, optimizing actions and reducing

• times. The sidewalk is now restored and ready to be

used.

Issue: Client wanted to be more responsive to service requests from citizens and increase control

• ver work performed by contractors

Chronic Care Disease Management - Solution

COMMITMENT

• Isabel’s wearable

tracks her activities in terms of #steps taken and monitors her heart rate real-time

• Isabel’s PCP coaches

Isabel on avoiding stressful situations and explains some breathing exercises for the future

• Wearable transmits data

to the IoT platform for Isabel

• Heart Rate Activity from

the wearable information is compared against pre- set thresholds for the program in real-time

• Isabel enrolls into a wellness

program sponsored by her health plan targeted at managing health for members with heart diseases

• As part of her enrollment she

provides approval for them to track and monitor her heart rate from her wearable

PROFILE

• Isabel is 41 years old
• Has Tachycardia heart condition
• Enrolled into the Heart Monitoring Program sponsored by her health

plan

Wearable- User – Plan Interaction

MONITOR TRACK COMPARE COMMUNICATE ENGAGE ACTIVITY TRACKER KPI’s

Meet Isabel

• When Isabel’s heart

registers palpitations leading to a pulse higher than threshold, the platform sends her a text message to encourage her to seek medical help

A health care provider focused, connected-devices solution that enables health care organizations to deliver high quality patient experiences in an accelerated fashion

Under the hood the platform has three key components

Wearable Devices Aggregators (MQTT Publisher) Tableau Server Mobile Alerts HDFS Stream Sink Queue In-memory DB IoT Platform API Stream Ingest Queue Batch Processing &Transformation Custom Events Processor Mule soft Restful Web services Health Plan Member and Enrollment Data (SalesForce) Real-time Updates Real-time Feed Sink Persistent Look-up Google Cloud Messaging REST APIs REST APIs ODBC Connection Daily Refresh Daily Refresh Real-time Extensible to other IoT protocols Extensible to using predictive analytics algorithms Extensible to integrating with other IoT devices Extensible to integrating with any

• ther downstream

systems Mule soft Restful Web services

Deloitte PaaS

LEGEND

Patient CRM (SalesForce) Source Real-time

Platform Dependent

1. An Unprecedented Opportunity 2. The Data Management Life Cycle 3. How disruptors are impacting industries? 4. Organizing to Succeed

Agenda

Key Competencies to enable Analytics

Functional Competencies Analytical and Visualization Tools

Expertise in Advanced analytics tools and techniques: Regression / Time-series / Classification / Clustering / Optimization/ Graph & Text Mining, Visualization Techniques

Communications and Strategic Thinking

Proficiency in simplifying analytical outputs and influencing key business stakeholders through effective communication of outputs

Knowledge of Function

Possession of work experience, knowledge and skill sets in specific functions

Enterprise Competencies Data Expertise

Expertise in small and Big Data Architectures, Modeling, Extraction, Transformation, and Loading, Data Management / Quality / Governance

Industry Expertise

Understanding of industry trends and key business drivers that impact measured metrics; ability to evaluate business issues by applying data-driven approaches

Technology / IT Expertise

Knowledge of Infrastructure Management / Support, Distributed Systems, Cloud Management, Big Data, Advanced Data Management and Systems Integration

The dimensions of a comprehensive Competency Center are much broader than just technology

• capabilities. A Competency Center needs various key skills to prioritize, manage, deliver and execute its

projects. It is challenging to find one person who has all of these competencies at the enterprise or functional level; however, there are different means to acquiring necessary talent

Ashish Verma – asverma@deloitte.com

Q&A